Duality Between the Local Score of One Sequence and Constrained Hidden Markov Model

We are interested here in a theoretical and practical approach for detecting atypical segments in a multi-state sequence. We prove in this article that the segmentation approach through an underlying constrained Hidden Markov Model (HMM) is equivalent to using the maximum scoring subsequence (also c...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Methodology and computing in applied probability Jg. 24; H. 3; S. 1411 - 1438
Hauptverfasser: Mercier, Sabine, Nuel, Grégory
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2022
Springer Nature B.V
Springer Verlag
Schriftenreihe:Methodology and Computing in Applied Probability
Schlagworte:
Adaptation
Algorithms
Business and Management
Economics
Electrical Engineering
Equivalence
Life Sciences
Markov chains
Mathematics
Mathematics and Statistics
Probability
Segmentation
Segments
Statistics
Maximum scoring subsequence
60J20
62M05
62C10
Local score
HMM
Forward/backward
60
Posterior distribution
Biological sequence analysis
62F15
forward/backward
maximum scoring subsequence
posterior distribution
biological sequence analysis
ISSN:1387-5841, 1573-7713
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:We are interested here in a theoretical and practical approach for detecting atypical segments in a multi-state sequence. We prove in this article that the segmentation approach through an underlying constrained Hidden Markov Model (HMM) is equivalent to using the maximum scoring subsequence (also called local score), when the latter uses an appropriate rescaled scoring function. This equivalence allows results from both HMM or local score to be transposed into each other. We propose an adaptation of the standard forward-backward algorithm which provides exact estimates of posterior probabilities in a linear time. Additionally it can provide posterior probabilities on the segment length and starting/ending indexes. We explain how this equivalence allows one to manage ambiguous or uncertain sequence letters and to construct relevant scoring functions. We illustrate our approach by considering the TM-tendency scoring function.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1387-5841
1573-7713
DOI:10.1007/s11009-021-09856-8